Tooth segmentation is an essential step in digital dental diagnosis and treatment planning, playing a crucial role across various dental specialties. Despite its importance, this process is fraught with challenges due to the high noise and low contrast inherent in 2D and 3D dental radiographic images. Both convolutional neural networks (CNNs) and transformers have shown promise in tooth image segmentation, yet each method has limitations in handling long-range dependencies and computational complexity. To address this issue, this paper introduces T-Mamba, integrating frequency-based features and shared bi-positional encoding into vision mamba to address limitations in modeling global features efficiently. Besides, we designed a gate selection unit to integrate two features in the spatial domain and one feature in the frequency domain adaptively. T-Mamba is the first to integrate frequency-based features into vision mamba, offering the flexibility to process both 2D and 3D tooth image data without the need for separate modules. Additionally, the TED, a large-scale public 2D dental X-ray dataset, has been presented in this paper. Extensive experiments demonstrated that T-Mamba achieved new SOTA performance on both the 3D dental CBCT dataset and our proposed 2D TED dataset. The code and models are publicly available athttps://github.com/isbrycee/T-Mamba.

Objects of daily life are designed to suit the human hand. Without major modifications to these objects and our environments, robots will need end-effectors with human hand-like configuration and dexterity to efficiently operate on them. Tight integration of tactile and proprioceptive sensors are also critical to ensure robust execution of manipulation policies without sacrificing range-of-motion. Reliability is also key, and a mechanically robust, easy to repair end-effector is important to minimize downtime. To meet these challenges, we designed a 13 degree-of-freedom anthropomorphic hand with over 1000 tactile sensing elements, named TRX-Hand5. Also embedded within are positional encoders and cable tension sensors to provide proprioceptive perception. TRX-Hand5 has a novel biomimetic topology with six small posture motors in the palm to replicate the function of intrinsic hand muscles and five large power motors in the forearm to play the role of forearm flexor muscles. The whole hand weighs 2.6 kg with its dimensions comparable to those of an adult male’s hand and is capable of actuating its fingertips at over 200°/s while exerting up to 22 N of force. The system can be disassembled in modules for easy maintenance.